In the digital age, organizations increasingly depend on a variety of data protection functions (e.g., backups, data replication, etc.) to protect and preserve their data. These organizations may operate under a variety of constraints (financial, technical, institutional, legal, etc.) which may increase their need for efficient implementation of their data protection solutions.
Traditional data protection solutions have implemented a variety of techniques to reduce overhead. For example, backup systems may take incremental backups by only capturing blocks that have changed since a previous incremental or full backup. Incremental backups may be smaller and faster to take than full backups and may therefore reduce data protection overhead.
Unfortunately, despite improvements in data protection technologies, volume level backup and replication solutions in cluster environments may still require significant overhead. For example, to meet a Recovery Point Objective (“RPO”), an enterprise may require cluster volume data to be backed up every ten minutes. The enterprise may use incremental backups to comply with the RPO, which is typically more efficient than taking full backups at each time increment. However, taking frequent incremental backups may still result in unacceptable overhead costs.
For example, in parallel cluster environments (i.e., environments where an application executes in parallel on multiple cluster nodes), traditional backup solutions may quiesce application Input/Output (“I/O”) during incremental backups to maintain consistency in the incremental backups. Frequent quiescing of application I/O may result in substantial performance degradation for the application and may be unacceptable to users. What is needed, therefore, is a more efficient and effective mechanism to provide data protection in parallel cluster environments.